Program
Speech Emotion Recognition I (SER I)
Mon-2-1-6 Augmenting Generative Adversarial Networks for Speech Emotion Recognition
Siddique Latif(University of Southern Queensland Australia/Distributed Sensing Systems Group, Data61, CSIRO Australia), Muhammad Asim(Information Technology University, Lahore), Rajib Rana(University of Southern Queensland), Sara Khalifa(Distributed Sensing Systems Group, Data61, CSIRO Australia), Raja Jurdak(Queensland University of Technology (QUT)) and Björn Schuller(University of Augsburg / Imperial College London)
Abstract:
Generative adversarial networks (GANs) have shown potential in learning emotional attributes and generating new data samples. However, their performance is usually hindered by the unavailability of larger speech emotion recognition (SER) data. In this work, we propose a framework that utilises the mixup data augmentation scheme to augment the GAN in feature learning and generation. To show the effectiveness of the proposed framework, we present results for SER on (i) synthetic feature vectors, (ii) augmentation of the training data with synthetic features, (iii) encoded features in compressed representation. Our results show that the proposed framework can effectively learn compressed emotional representations as well as it can generate synthetic samples that help improve performance in within-corpus and cross-corpus evaluation.
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